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2,113 results for “Very High Resolution”
Ultra-high-resolution modified RGB UAV-imaging of Alternaria solani
<p>This dataset is collected from both symptomatic and non-symptomatic plants during the growing seasons of 2019 and 2022, on 40x20 m experimental fields in Lemberge (Merelbeke), Belgium (50.986544°N, 3.774066°E) using a DJI M600 PRO unmanned aerial vehicle equiped with a modified Sony Alpha 7III camera with 135 mm lens. The field trial is conducted in analogy to the method described by Van De Vijver et al. (2020, 2022), using two different cultivers, Spunta (2019) and Fontane (2022) respectively. The dataset of 2019 comprises data from three different flights (3, 6 and 9 days after inoculation) and the dataset of 2022 from four different flights (5,7, 9 and 13 days after inoculation). </p> <p>This dataset consists out of 7660 patches of 256x256 pixels, cropped out of the original images, labeled and sorted in two categories (1: Alternaria, 0: no Alternaria), accompagned by a csv file containing the following information:</p> <ul> <li>Original patch name</li> <li>Random patch name (used during the labeling process)</li> <li>Row patch number</li> <li>Column patch number</li> <li>Block number, column block number and row block number</li> <li>Original mage name</li> <li>Coordinates of original image: latitude, longitude, altitude</li> <li>Date of flight</li> <li>Label (0: no Alternaria, 1: Alternaria)</li> </ul> <p>More detailed information about this dataset (both the collection and the preprocessing) can be found in the corresponding article 'Ultra-high-resolution UAV-Imaging and Supervised Deep Learning for Accurate Detection of Alternaria Solani in Potato Fields.' </p> <p> </p> <p>If you use this dataset, please refer to the related journal paper as follows: "Wieme J, Leroux S, Cool SR, Van Beek J, Pieters JG and Maes WH (2024) Ultra-highresolution UAV-imaging and supervised deep learning for accurate detection of Alternaria solani in potato fields. Front. Plant Sci. 15:1206998. doi: 10.3389/fpls.2024.1206998"</p> <p> </p> <p>This dataset was gathered within the Proeftuin Smart Farming 4.0 project (180503) within the Industry 4.0 Living Labs with funding from Flanders innovation & entrepreneurship (VLAIO, Belgium) and in the Horizon 2020 project SmartAgriHubs - Connecting the dots to unleash the innovation potential for digital transformation of the European agrifood sector with funding from the European Union under grant agreement No. 818182. Jana Wieme is funded by grant 1SE3921N of Research Foundation Flanders (FWO).</p> <p> </p>
High resolution rice yield data of Jiangsu, China
<p>This dataset presents rice yields of Jiangsu Province, China during 2001-2020, the spatial resolution is 1 km. </p> <p>The dataset was generated using Random Forest Regression algorithm and multi-phase remote sensing data, the accuracy is <span>R2 = 0.65, RMSE = 388.79 kg/ha, and rRMSE = 4.48%.</span></p>
Design files for a low-cost high-resolution imaging device for hyphae in soil
<p>This dataset contains the stereolithography (STL) files for the 3D-printed and cut parts of a low-cost high-resolution imaging device for hyphae in soil called <em>Hyphascope</em>. The design of <em>Hyphascope</em> was adopted from the 3D printer i3 MK3S+ by Prusa Research, with a digital microscope camera (DMC; 600× magnification) replacing the filament extruder. Repeated imaging of a soil profile with the imaging device enables researchers to observe and quantify changes in the amount, distribution, and morphology of hyphae.</p> <p>The parts were created and modified using <a href="https://www.freecad.org">FreeCAD</a> (version 0.20). STL files for the original parts are distributed under the Creative Commons Attribution 4.0 International License, STL files for the remixed parts under the GNU General Public License v2.0. For a detailed description on how to prepare and assemble the parts see <a href="https://doi.org/10.17504/protocols.io.bp2l6xo3zlqe/v1">this protocol on protocols.io</a>. For information on the development, limitations, and expected outcomes of the protocol, see <a href="https://doi.org/10.1371/journal.pone.0318083">this article</a> published in PLOS ONE.</p> <p> </p> <div> <h2>STL files of 3D-printed parts</h2> <h3>Original parts</h3> </div> <div> <div> <ul> <li><em>dmc-attachment.stl</em></li> <li> <div><em>dmc-attachment-gear.stl</em></div> </li> <li><em>dmc-attachment-gear-wider.stl</em> (optional part)<em><br></em></li> <li><em>dmc-holder-back.stl</em></li> <li><em>dmc-holder-front.stl</em></li> <li><em>f-axis-motor-gear.stl</em></li> <li><em>f-axis-spring-end.stl</em></li> <li><em>f-axis-tighteners.stl</em></li> <li><em>frame-foot-inserts.stl</em></li> <li> <div><em>frame-foot-left.stl</em></div> </li> <li> <div><em>frame-foot-right.stl</em></div> </li> <li> <div><em>frame-hat.stl</em></div> </li> <li> <div><em>frame-hat-insert.stl</em></div> </li> </ul> </div> <h3>Remixed parts originally designed by Prusa Research</h3> <p><em>The five parts below are <strong>remi</strong></em><strong><em>xed from <a href="https://www.printables.com/model/57217-i3-mk3s-printable-parts">i3 MK3S+ printable parts</a> </em></strong><em>and </em><strong><em>re-distributed under the <a href="http://www.gnu.org/licenses/old-licenses/gpl-2.0.html">GNU General Public License v2.0</a></em></strong><em>.</em></p> </div> <ul> <li> <div><em>dmc-carriage-back.stl</em> (Remix of <em>x-carriage-back.stl</em>; the design was largely modified to fit the DMC including changes to the shape and screw hole placement; the inserts for the linear bearings have the most resemblence to the original part.)</div> </li> <li><em>dmc-carriage-front.stl </em>(Remix of <em>x-carriage.stl</em>; the design was largely modified to fit the DMC including changes to the shape and screw hole placement; the inserts for the linear bearings have the most resemblence to the original part.)</li> <li><em>x-end-idler-mod.stl</em> (Remix of <em>x-end-idler.stl</em>; the height was increased by 20 mm.)</li> <li><em>x-end-motor-mod.stl</em> (Remix of <em>x-end-motor.stl</em>; the height was increased by 20 mm and the counterbores of the three motor screws were moved to the opposite side.)</li> <li><em>z-axis-top-mod.stl </em>(Remix of <em>z-axis-top.stl</em>; 14.8 mm-long spacers were added.)</li> </ul> <h3>Parts designed by Prusa Research</h3> <ul> <li> <div><em>z-axis-bottom.stl</em> (available from <a href="https://www.printables.com/model/57217-i3-mk3s-printable-parts" target="_blank" rel="noopener">Printables</a>)</div> </li> <li> <div><em>z-screw-cover.stl</em> (available from <a href="https://www.printables.com/model/57217-i3-mk3s-printable-parts" target="_blank" rel="noopener">Printables</a>)</div> </li> </ul> <p> </p> <div> <h2>STL files of cut parts</h2> <h3>Original parts</h3> </div> <ul> <li><em>box-bottom.stl</em></li> <li><em>box-hook.stl</em></li> <li> <div><em>box-lid.stl</em></div> </li> <li> <div><em>box-lid-frame.stl</em></div> </li> <li><em>box-lid-valve-base.stl</em></li> <li><em>box-wall.stl</em></li> <li><em>box-wall-cables.stl</em></li> <li> <div><em>frame.stl</em></div> </li> </ul> <p> </p>
Semantic segmentation model of construction waste landfill based on high-resolution satellite images
<p>CWLD_model project shows scripts and instructions on how to use this dataset (<a href="../records/10686118">https://zenodo.org/records/10686118</a>) to train a segmentation model. requirements.txt files provide the libraries you need to run your project. The README.md document details the deployment process and features of each module.</p> <p>You can also visit the GitHub page for scripts and instructions on how to use this dataset for visualizing and plotting basic statistics. The models and the code to execute them are released on <a href="https://github.com/huangleinxidimejd/CWLD_Model">https://github.com/huangleinxidimejd/CWLD_Model</a>.</p> <h2>Training details</h2> <p>The model was trained with two GPUs, an Nvidia GeForce RTX 2080Ti, and the following parameters:</p> <ul> <li>'train_batch_size': 4,</li> <li>'val_batch_size': 4,</li> <li>'train_crop_size': 512,</li> <li>'val_crop_size': 512,</li> <li>'lr': 0.001, # the learning rate used during training. It determines how quickly the model learns from the data</li> <li>'Epoch Times': 200,</li> <li>'gpu': correct,</li> <li>'weight_decay': 5E-4,</li> <li>'Momentum': 0.9,</li> <li>'print_freq': 100,</li> <li>'predict_step': 5,</li> </ul> <h2>usage</h2> <ul> <li>After downloading the dataset from Zenodo, place the train and val files from the Deep Learning Datasets file into the data folder of the CWLD semantic segmentation model.</li> <li>Open: CWLD_ Open the root directory in CWLD_model/dataset/ and start training with the WasteSeg_Train.py file. The modelss module provides five convolutional networks, Improved_DeeplabV3_plus, PSPNet, ResNet, SegNet, and UNet, which can be selected and modified accordingly.</li> <li>The utils package provides a large number of data processing tools to use.</li> <li>The trained model can be predicted from a EvalSeg.py file.</li> </ul>
Attribution of 2022 August Heavy Precipitation Event in South Korea Using High-resolution Pseudo Global Warming Simulations: Sensitivity to Vertical Temperature Changes
<p>Post-processed CPM simulation datasets used for the paper "Attribution of 2022 August Heavy Precipitation Event in South Korea Using High-resolution Pseudo Global Warming Simulations: Sensitivity to Vertical Temperature Changes".</p>
High-resolution microclimatic grids for the Bohemian Forest Ecosystem
<p>Here, we provide spatially continuous, high-resolution (5 m) microclimate grids covering all 923 km<sup>2</sup> of the Bohemian Forest Ecosystem (BFE), i.e. the complete area of the Šumava (Czech Republic) and Bavarian Forest (Germany) National Parks.</p> <p>To derive these grids, we have established a dense network of 288 microclimatic stations that continuously measured air, near-surface, and soil temperature every 15 minutes from 12th October 2019 to 11th October 2020. We combined the measured microclimate temperature with LiDAR derived land surface topography and forest structure through boosted spatial generalized additive models (GAMs).</p> <p>We validated the resulting microclimatic grids with an independent network of forest weather stations and compared these microclimatic grids with the SoilTemp (soil temperature), ForestTemp (near-ground forest understorey temperature), and downscaled ERA5-Land (air temperature). The developed BFE microclimatic grids were closer to independently measured temperatures than any other alternative and captured high microclimatic variability controlled jointly by land surface topography and forest structure. </p> <p>Our microclimatic grids represent accurate, high-resolution spatial variation of mean annual soil temperature, mean, maximum and minimum air temperature at two heights, and growing degree days at 200 cm. </p> <p><strong>The dataset contains 8 microclimatic grids (GeoTIFF format, coordinate system EPSG 31468, resolution 5 m).</strong></p> <p>Extent: 4587063, 5399139: 4646023, 5451289</p> <p>The name of the file is “name.tif”, where name represents the abbreviation of the microclimatic variable (see names below).</p> <p>The values are in °C (°C d for GDD), the data can be readily imported into standard geographical information system software (e.g., QGIS) or accessed in a statistical software (e.g., R). The datasets do not include colour schemes.</p> <p><strong>Measured variable (depth/height) </strong><br> - Microclimatic variable Abbreviation (Units)</p> <p><strong>Soil temperature (-8 cm) </strong> <br> - Mean temperature = mean temperature T.soil_8_cm.mean (°C)<br> <br><strong>Near-ground air temperature (15 cm) </strong> <br> - Mean temperature = mean temperature T.air_15_cm.mean (°C)<br> - Maximum temperature = 95<sup>th</sup> percentile of daily maximum temperatures T.air_15_cm.max.95p (°C)<br> - Minimum temperature = 5<sup>th</sup> percentile of daily minimum temperatures T.air_15_cm.min.5p (°C)<br> <br><strong>Air temperature (200 cm)</strong> </p> <p> - Mean temperature = mean temperature T.air_200_cm.mean (°C)<br> - Maximum temperature = 95<sup>th</sup> percentile of daily maximum temperatures T.air_200_cm.max.95p (°C)<br> - Minimum temperature = 5<sup>th</sup> percentile of daily minimum temperatures T.air_200_cm.min.5p (°C)<br> - Growing degree days = sum of degree days above base temperature (base 5°C) T.air_200_cm.GDD5 (°C d)</p> <p><strong>BFE_script_and_data.zip</strong> contains</p> <p> - Microclimatic variables, topography and forest structure variables for all stations used for modelling (BFE_data.RData).<br> - Script BFE_microclimate_maps_model_script.R used for statistical modelling and prediction. <br> - microclimate2predict.csv - list of microclimate variables for prediction used in the script.</p> <p>The data used for prediction cannot be made publically available. <br>The LIDAR data can be obtained from Administration of Bavarian Forest National Park and Šumava National Park Administration. The LIDAR-derived topography and forest structure rasters can be obtained upon request from the authors.</p> <p>Detailed description will be available in a manuscript.</p> <p><strong>Version 2</strong> insludes improved rasters, the GeoTIFFs include aplha channel for transparency of pixels with NA and full script used for processing.</p> <p> </p>
Data for "Detection of metabolite-protein interactions in complex biological samples by high-resolution relaxometry: towards interactomics by NMR"
<p>Raw NMR data for relaxometry experiments, divided by donor sample. For every donor sample 2 or 3 different samples were used in order to record data at 19 different magnetic fields.</p> <p>Data from fast field-cycling relaxometry. All the data is in one xlsx file, divided by donor sample.</p> <p>Relaxometry results for alanine, lactate, creatinine and glutamine, obtained from the fitting of their relaxation decays recorded at 19 different fields, divided by donor sample.</p>
Outputs of the Jupyter Notebook - Met Office UKV high-resolution atmosphere model data
<p>The dataset contains the outputs of the notebook "Met Office UKV high-resolution atmosphere model data" published in the urban sensors section of The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Samantha V. Adams (author), Met Office Informatics Lab, <a href="https://github.com/svadams">@svadams</a></p> </li> <li> <p>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>Met Office Informatics Lab (creator)</p> </li> <li> <p>Microsoft (support)</p> </li> <li> <p>European Regional Development Fund (support)</p> </li> </ul> <p><em>Dataset authors</em></p> <ul> <li> <p>Met Office</p> </li> </ul> <p><em>Dataset documentation</em></p> <ul> <li> <p>Theo McCaie. Met office and partners offer data and compute platform for covid-19 researchers. URL: <a href="https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f">https://medium.com/informatics-lab/met-office-and-partners-offer-data-and-compute-platform-for-covid-19-researchers-83848ac55f5f</a>.</p> </li> </ul> <p> </p>
High-resolution, mixed layer NCP estimates and ancillary data from the Central and Eastern North American Arctic: 2015, 2018, 2019
<p><strong>Dataset overview</strong></p> <p>This dataset contain ship-based, high-resolution (underway) estimates of mixed layer net community production (NCP) and ancillary data from three summertime cruises in the Central and Eastern North American Arctic in 2015, 2018 and 2019. NCP estimates were derived from underway O2/Ar observations, obtained using ship-based membrane inlet mass spectrometry. Ancillary data include geospatial information (time, location), surface and depth-resolved hydrography and biogeochemical observations, and select output from a simulation of an oceanographic circulation model, based on the NEMO framework.</p> <p>Please cite as:</p> <p>Izett, R. and Tortell, P. 2021. High-resolution, mixed layer NCP estimates and ancillary data from the Central and Eastern North American Arctic: 2015, 2018, 2019 (Dataset). Zenodo. https://doi.org/https://doi.org/10.5281/zenodo.5593381.</p> <p>This dataset is supplement to:</p> <p>Izett, R. W., Castro de la Guardia, L., Chanona, M., Myers, P. G., Waterman, S, and Tortell, P. D. Impact of vertical mixing on summertime net community production in Canadian Arctic and Subarctic waters: Insights from in situ measurements and numerical simulations. </p> <p> </p> <p><strong>Abstract</strong></p> <p>We present ΔO<sub>2</sub>/Ar-based estimates of mixed layer net community production (NCP) from three summer cruises in the North American Arctic and Subarctic oceans. Coupling shipboard underway and discrete observations with output from an ocean circulation model, we correct the NCP estimates for vertical mixing fluxes impacting the surface O<sub>2</sub> budget. Large positive mixing fluxes, exceeding 100 mmol O<sub>2</sub> m<sup>-2</sup> d<sup>-1</sup>, were derived in regions of strong wind-driven mixing, such as the Labrador Sea, and in the physically-dynamic Canadian Arctic Archipelago. In contrast, flux corrections were small (<10 mmol O<sub>2</sub> m<sup>-2 </sup>d<sup>-1</sup>, on average) in the density-stratified Baffin Bay, where mixing was low, and parts of the well-mixed Hudson Strait, where vertical O<sub>2</sub> gradients were weak. The distribution of corrected NCP was highly heterogenous across the study region, reflecting varying contributions of nutrient supply, freshwater input and sea ice dynamics. Elevated NCP was apparent in the Labrador Sea, Hudson Strait, and nearshore regions influenced by glacial meltwater and recent ice retreat. Low NCP and localized net heterotrophy occurred in Baffin Bay, and near strong freshwater and organic matter sources in Hudson Bay and the Queen Maud Gulf. A multiple linear regression model developed using available oceanographic data explained ~58 % of the observed NCP variability. Our work demonstrates the spatially explicit influence of vertical mixing on ΔO<sub>2</sub>/Ar-based NCP calculations across varied hydrographic conditions, and presents a novel approach to account for this process. This study contributes new knowledge of biological productivity distributions in under-sampled, rapidly changing, high-latitude waters.</p> <p> </p> <p><strong>Lay summary</strong></p> <p>Net community production (NCP; i.e., net organic matter production) constrains the ocean’s ability to support marine ecosystems and remove carbon dioxide from the atmosphere. A common approach to estimating NCP involves measurements of upper ocean oxygen (O<sub>2</sub>) concentrations. However, while vertical mixing may be a significant component of the surface water O<sub>2</sub> budget in some regions, applications of this approach typically do not quantify the magnitude of this flux, which can lead to potentially inaccurate NCP estimates. In this paper, we introduce a method combining ship-based measurements and the output from an ocean circulation model to refine NCP calculations for vertical mixing effects in North American Arctic and Subarctic oceans. The dataset reveals high NCP in the Labrador Sea (Inuktitut: <em>Lâbradorip Imappinga</em>), North Atlantic, Hudson Strait (<em>Ikirasarjuaq</em>) and northern Canadian Arctic Archipelago (CAA), and low values in Baffin Bay (<em>Saknirutiak Imanga</em>) and southern CAA. Riverine freshwater input to Hudson Bay (<em>Tasiujarjuar</em>) and the Queen Maud Gulf (<em>Ugjulik</em>) can reduce local NCP, while glacial meltwater may stimulate NCP elsewhere. Overall, this work provides a new NCP dataset in an under-sampled region. Similar studies will be necessary to document changes in biological productivity in response to changing environmental conditions in polar waters.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This work was supported by the ArcticNet and MEOPAR Networks of Centres of Excellence Canada, Polar Knowledge Canada, the Natural Sciences and Engineering Research Council of Canada (NSERC) and Compute Canada.</p> <p>Hydrography and ancillary oceanographic data were provided by the Amundsen Science group of Université Laval.</p> <p>The model simulation was run by P. Myers (University of Alberta).</p> <p>The underway gas data were collected by R. Izett & P. Tortell (University of British Columbia)</p> <p>All data were archived by R. Izett. </p> <p> </p> <p><strong>Files and variables:</strong></p> <p>data_yyyy (data provided in NetCDF and Matlab format; "yyyy" denotes sampling year): Ship-board observations and derived quantities.</p> <table> <tbody> <tr> <td><em>Variable</em></td> <td><em>Description </em></td> <td><em>Unit</em></td> </tr> <tr> <td>time</td> <td>UTC YYYY Julian Day (year-day since YYYY-01-01)</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Decimal degrees E</td> </tr> <tr> <td>dist</td> <td>Along-track distance</td> <td>km</td> </tr> <tr> <td>reg_index</td> <td>Regional index</td> <td> </td> </tr> <tr> <td>region_mask_lat</td> <td>Latitude for region indices mask</td> <td>Decimal degrees N</td> </tr> <tr> <td>region_mask_long</td> <td>Longitude for region indices mask</td> <td>Decimal degrees E</td> </tr> <tr> <td>region_mask</td> <td>Regional masks</td> <td> </td> </tr> <tr> <td>sst</td> <td>Sea surface temperature measured in the instrument laboratory</td> <td>deg. C</td> </tr> <tr> <td>sal</td> <td>Sea surface salinity measured in the instrument laboratory</td> <td> </td> </tr> <tr> <td>chl_fluor</td> <td>Calibrated mixed layer Chl a fluorescence in the instrument laboratory</td> <td>(mg Chl a)/m3</td> </tr> <tr> <td>do2ar</td> <td>Biological O2 saturation anomaly, deltaO2/Ar</td> <td>%</td> </tr> <tr> <td>kwo2</td> <td>Weighted O2 gas transfer velocity</td> <td>m/d</td> </tr> <tr> <td>bioflux_ncp</td> <td>Bioflux-NCP</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>cor_ncp</td> <td>corrected-NCP</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>uw_kz</td> <td>Underway model-based eddy diffusivity at the base of the mixed layer</td> <td>m2/s</td> </tr> <tr> <td>bling_ncp</td> <td>BLING model-based mixed layer NCP, matched to underway cruise time/position</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>prof_time</td> <td>UTC 2015 Julian Day (year-day since 2015-01-01) at CTD profile stations</td> <td>UTC Days</td> </tr> <tr> <td>prof_lat</td> <td>Latitude N at CTD profile stations</td> <td>Decimal degrees N</td> </tr> <tr> <td>prof_long</td> <td>Longitude E at CTD profile stations</td> <td>Decimal degrees E</td> </tr> <tr> <td>prof_dist</td> <td>Along-track distance at CTD profile stations</td> <td>km</td> </tr> <tr> <td>prof_reg_index</td> <td>Regional index at CTD profile stations</td> <td> </td> </tr> <tr> <td>prof_do2bdz</td> <td>Subsurface O2b gradient, dO2B/dZ at CTD profile stations</td> <td>mmol O2/m4</td> </tr> <tr> <td>prof_mld</td> <td>Mixed layer depth, calculated at CTD profile stations</td> <td>m</td> </tr> <tr> <td>prof_pycnocline_dep</td> <td>Pycnocline depth, calculated at CTD profile stations</td> <td>m</td> </tr> </tbody> </table> <p> </p> <p>nemo_yyyy (data provided in NetCDF format only; "yyyy" denotes sampling year): 4-dimensional gridded NEMO model output. </p> <table> <tbody> <tr> <td>Varaible</td> <td>Description </td> <td>Unit</td> </tr> <tr> <td>time</td> <td>Model time, UTC YYYY Julian Day (year-day since YYYY-01-01)</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Model Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Model Decimal degrees W</td> </tr> <tr> <td>depth_grid_kz</td> <td>kz depth</td> <td>m</td> </tr> <tr> <td>depth_grid</td> <td>depth</td> <td>m</td> </tr> <tr> <td>kz</td> <td>Eddy diffusivity coefficient</td> <td>m2/s</td> </tr> <tr> <td>T</td> <td>Temperature</td> <td>deg-C</td> </tr> <tr> <td>sal</td> <td>Salinity</td> <td> </td> </tr> <tr> <td>oxy</td> <td>Oxygen concentration</td> <td>mol O2/m3</td> </tr> </tbody> </table> <p> </p> <p>ArcticNet2003-2014_derived_quantities (data provided in NetCDF format only): Derived quantities from ArcticNet sampling.</p> <table> <tbody> <tr> <td>Varaible</td> <td>Description </td> <td>Unit</td> </tr> <tr> <td>time</td> <td>UTC; Days since 2010-01-01</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Decimal degrees E</td> </tr> <tr> <td>reg_index</td> <td>Regional index</td> <td> </td> </tr> <tr> <td>prof_do2bdz</td> <td>Subsurface O2b gradient, dO2B/dZ at CTD profile stations</td> <td>mmol O2/m4</td> </tr> <tr> <td>prof_mld</td> <td>Mixed layer depth, calculated at CTD profile stations</td> <td>m</td> </tr> <tr> <td>prof_pycnocline_dep</td> <td>Pycnocline depth, calculated at CTD profile stations</td> <td>m</td> </tr> </tbody> </table> <p> </p> <p> </p>
Remote Sensing VQA - High Resolution (RSVQA HR)
<p>Remote sensing images contain a wealth of information which can be useful for a wide range of tasks including land cover classification, object counting or detection. However, most of the available methodologies are task-specific, thus inhibiting generic and easy access to the information contained in remote sensing data. As a consequence, accurate remote sensing product generation still requires expert knowledge. With RSVQA, we propose a system to extract information from remote sensing data that is accessible to every user: we use questions formulated in natural language and use them to interact with the images. With the system, images can be queried to obtain high level information specific to the image content or relational dependencies between objects visible in the images. Using an automatic method, we built two datasets (using low and high resolution data) of image/question/answer triplets. The information required to build the questions and answers is queried from OpenStreetMap (OSM). The datasets can be used to train (when using supervised methods) and evaluate models to solve the RSVQA task.</p> <p>This page is about the high resolution dataset.</p>
5D-NP-MATER_MDO - Open Dataset for "Novel, High-Resolution, Subtractive Photoresist Formulations for 3D Direct Laser Writing Based on Cyclic Ketene Acetals"
<p>This is the open dataset for the paper: "Marco Carlotti*, Omar Tricinci, Virgilio Mattoli*, Novel, High-Resolution, Subtractive Photoresist Formulations for 3D Direct Laser Writing based on Cyclic Ketene Acetals, Advanced Materials Technologies, On line (2022) [DOI: 10.1002/admt.202101590] "</p> <p>This include the Supplementary Information file ("SI.pdf") , all the source material used for the paper preparation and more. </p> <p>For each folder (sub-dataset) there is a corresponding readme file describing the content and including metadata.</p> <p> </p>
High resolution soil moisture and soil temperature data during Hurricane Florence, 2018, over the Carolina region (U.S.)
<p>We set the study domain over the U.S. east coast to cover the Carolinas and the regions that were affected by Hurricane Florence. Therefore, the selected domain covered the area between -86º to -75º longitude and 30º to 40º latitude. We should note that Hurricane Florence made landfall in the Carolinas on September 14, 2018, as a Category 1 storm. Hurricane reports indicate that “Hurricane Florence made landfall near Wrightsville Beach, North Carolina at 7:15 AM EDT (1115 UTC) on September 14 with estimated maximum winds of 90 mph (150 km/h), and a minimum central pressure estimate of 958 millibars. Winds gusts topping 105 mph (169 km/h) were reported in the Outer Banks of North Carolina.” Rainfall from Florence, as per the initial reports, suggest possible new records for North Carolina (breaking the record set by Hurricane Floyd in 1999).</p> <p>we used the latest development of the high-resolution land surface assimilation system (HRLDAS) that was retrieved from the Github repository (<a href="https://github.com/NCAR/hrldas-release">https://github.com/NCAR/hrldas-release</a>). The model was coupled to the Noah land surface modeling system and used the multi-layer soil model, complex canopy resistance with the Penman method for calculating evapotranspiration, and frozen ground physics for the simulations.</p> <p> The atmospheric forcing including 2-meter air temperature, shortwave, and long-wave radiation, 2-meter wind speed, 2-meter specific humidity, and surface pressure data was obtained from the NCEP-DOE Reanalysis 2 (available from <a href="https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html">psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html</a>). For the precipitation, we used the high-resolution GCIP/EOP surface precipitation NCEP/EMC gridded data (Stage IV) with 4 km of grid spacing. All the forcing data have been retrieved at an hourly frequency from 2016 to 2019. The model was configured with the initial soil moisture and soil temperature conditions at 4 depths (0-10, 10-40, 40-100, 100-200 cm), retrieved from the NCEP data. Other initialization fields including skin temperature and water equivalent snow depth were retrieved from NCEP. The NCEP reanalysis data, however, does not provide “plant canopy surface water” data which is required as an initialization field. This data was retrieved from the NLDAS dataset. The first two years of the model run (2016 and 2017) were considered as the spin-up, and the outcome during 2018 was used for further analysis and public release.</p>
BRAVES database Version 1.1 (REVISED): multispecies and high spatiotemporal resolution database of vehicular emissions in Brazil.
<p>The BRAzilian Vehicular Emissions inventory Software (BRAVES) database is a multispecies and high spatiotemporal resolution database of vehicular emissions in Brazil. We provide this database using a spatial disaggregation based on road density, temporal disaggregation using vehicular flow profiles, and chemical speciation based on SPECIATE database from the United States Environmental Protection Agency. We provide netCDF files with spatial resolution of 0.05x0.05 and annual emissions from 2013 to 2019. Files are divided by vehicle type (light, commercial-light, motorcycles, and heavy). We also provide the total vehicular emissions (sum of emissions from all vehicle types). The database contains emissions of 41 chemical species, such as ACET, ACROLEIN, ALD2, BENZ, BUTADIENE13, CH4, CO, CO2, ETH, ETHA, ETHY, ETOH, FORM, ISO, N2O, NAPH, NO, NO2, PAL, PCA, PCL, PEC, PFE, PK, coarse mode primary PM (PMC), PMG, PMN, unspeciated PM2.5 (PMOTHR), PNA, PNH4, PNO3, POC, PRPA, PSI, PSO4, PTI, SO2, TERP, TOL, VOC, and XYLMN. Codes from BRAVES are available by registering at <a href="https://hoinaski.prof.ufsc.br/BRAVES/">https://hoinaski.prof.ufsc.br/BRAVES/</a> and <a href="https://github.com/leohoinaski/BRAVES">https://github.com/leohoinaski/BRAVES</a>, where users can access instructions to run the database and download the input files.</p> <p>In this updated version from the first BRAVES database version, we have preserved estimates of ETOH and RCHO from CETESB. Brazil has a unique chemical signature of the chemical composition due to the biofuels (27% of gasoline is ethanol and 7% of diesel is bio-diesel). The emissions of C2H4O (ALD2), CH2O (FORM), and C3H6O (ACET) have been derived from RCHO emissions. We have used US-EPA Speciate to speciate compounds only when local emission factors of RCHO and ETOH are not available, such as in the case of motorcycles and heavy vehicles.</p> <p>We have also included emissions of PM2.5 (PMFINE), speciating coarse PM emissions from brake and tires (40%), road wear (53%), road dust resuspension (17%), and exhaust emissions (100%). Pixel center coordinates (longitude, latitude), pixel area (AREA), and pixel local time zone (LTZ) shift from UTC has been added to the netCDF files.</p> <p>This database has been currently part of the preprint currently under review for the journal ESSD (https://doi.org/10.5194/essd-2022-74).</p> <p> </p> <p>Files description:</p> <p>BRAVESdatabaseAnnual_BR_(typeEmiss)_(Vehicle Type)_(resolution)_(year).nc - Annual emissions in Brazil by vehicle type and 0.05x0.05 degree of resolution.</p> <p>Domain:</p> <p>lati = -36 #(Brazil) #lati = int(round(bound.miny)) # Initial latitude</p> <p>latf = 8 #(Brazil) #latf = int(round(bound.maxy)) # Final latitude</p> <p>loni = -76 #(Brazil) #loni = int(round(bound.minx)) # Initial longitude</p> <p>lonf = -32 #(Brazil) #lonf = int(round(bound.maxx)) # Final longitude</p> <p>deltaX = 0.05 # Grid resolution/spacing in x direction</p> <p>deltaY = 0.05 # Grig resolution/spacing in y direction</p> <p> </p> <p>typeEmiss:</p> <p>'TOTAL' = Total emissions/sum of emissions types<br> 'Exhaust' = Only exhaust emissions<br> 'non-exaust' = Only non-exhaust emissions<br> 'non-exaustMP' = Only Particulate Matter non-exhaust emissions<br> 'non-exaustMP_no_resusp'= Only Particulate Matter non-exhaust emission excluding road resuspension </p>
CRIRES high-resolution near-infrared spectroscopy of diffuse interstellar band profiles
<p>This archive contains data used for the paper:</p> <p>CRIRES high-resolution near-infrared spectroscopy of diffuse interstellar band profiles. Detection of 12 new DIBs in the YJ band and the introduction of a combined ISM sight line and stellar analysis approach</p> <p>Paper-DOI: 10.1051/0004-6361/202142990</p> <p>It contains reduced oCRIRES spectra. For more details on the reduction see the paper.</p>
Dataset of polygons with the contour of 900 juniper shrubs used to track shrub growth from 1977 to 2020 in Sierra Nevada (Spain) using very high resolution aerial and satellite RGB images.
<p><strong>This database provides as polygons the contours of 900 juniper shrubs (<em>Juniperus communis L.</em> and <em>Juniperus sabina L.</em>) along 5 decades (years 1977, 1984, 2001, 2010 and 2020). The contour of each of 900 shrubs manually mapped using the Google Satellite composite for the year 2020) was tracked back in time using orthophotos provided by REDIAM. Contours were obtained by manual annotation as polygon shapefiles in QGIS 3.10.3. Additionally, for the year 2020, the polygons were characterized with five attributes that gather ecological information: Morphotype (Hemispherical, Striped, Senescent, With rock), Presence of surrounding vegetation (Bare Soil, Surrounding Vegetation), Presence of nearby human land-uses (Surrounded by human facilities within 250 meters, Non-anthropized environment) Health status (as percentage of canopy cover with brown foliage: values between 0-5, where 0 corresponds to 100% photosynthetically active cover, decreasing the photosynthetically active cover until category 5 which corresponds to 100% damaged cover), and the subjective annotation certainty of the GIS technician (values between 0-5, where the value 0 corresponds to a very uncertain annotation up to the value 5 which corresponds to a fairly certain annotation). </strong></p>
High-resolution maps of material stock and population in Germany from 1985 to 2018
<p>Global societal material stocks such as buildings and infrastructure accumulated rapidly within recent decades, along with population growth. Material stocks constitute the physical basis of most socio-economic activities and services, such as mobility, housing, health, or education. The dynamics of stock growth, and its relation to the population that demands those services, is an essential indicator for long-term societal resource use and patterns of emissions. The creation of societal material stock creates path dependencies for future resource use, with an important impact on how the transformation towards sustainable societies can succeed.</p> <p>This dataset features detailed maps of material stock and population for Germany on a 30m grid. The data is based on recent maps of material stock and building volume (compare to Haberl et al. 2021, doi: 10.1021/acs.est.0c05642), recent and historic census data, and a time series of Landsat TM, ETM+, and OLI Earth Observation data.</p> <p><strong>Temporal extent</strong></p> <p>The data contains annual maps from 1985 to 2018.</p> <p><strong>Data format and units</strong></p> <p>Per German federal state, the data come in tiles of 30x30km. The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Please consider the generation of image pyramids before using *.vrt files.</p> <p>All image data has 34 bands, where band 1 is data for 1985, and band 34 is data for 2018.</p> <p>The dataset features</p> <ul> <li>population (Scaled by 100 to reduce data storage size. Divide by 100 to get people per cell)</li> <li>mass (in tons) of … <ul> <li>total material stock <ul> <li>… material stock in buildings <ul> <li>… in commercial and industrial buildings</li> <li>… in multi-family residential buildings</li> <li>… in single-family residential buildings</li> <li>… in high-rise buildings</li> <li>… in lightweight buildings</li> </ul> </li> <li>… material stock in road infrastructure</li> <li>… material stock in rail infrastructure</li> <li>… material stock in other infrastructure</li> </ul> </li> </ul> </li> </ul> <p>Material stock in high-rise and lightweight buildings is not featured in the corresponding publication due to its overall negligible amount. It is, however, included here for completeness.</p> <p><strong>Further information</strong></p> <p>For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p> <p><strong>Corresponding publication</strong></p> <p>Schug, F., Frantz, D., Wiedenhofer, D., Virág, D., Haberl, H., van der Linden, S., Hostert, P. (in rev.): High-resolution mapping of 33 years of material stock and population growth in Germany. Journal of Industrial Ecology</p> <p><strong>Funding</strong></p> <p>This research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).</p> <p> </p>
Dataset of "An end-to-end KNN-based PTV approach for high-resolution measurements and uncertainty quantification"
<p>Dataset of the article "An end-to-end KNN-based PTV approach for high-resolution measurements and uncertainty quantification" (https://doi.org/10.1016/j.expthermflusci.2022.110756). Local similarity between non-time-resolved snapshots is enforced by KNN to extract high-resolution velocity fields and estimate the uncertainty of the measurements.</p> <p>The codes processing data here are on https://github.com/erc-nextflow/KNN-PTV.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085, NEXTFLOW).</p>
Raw Data - High resolution electrochemical additive manufacturing of microstructured active materials: case study of MoSx as a catalyst for the hydrogen evolution reaction
<p>The dataset contains raw data that complements the article:</p> <p>High resolution electrochemical additive manufacturing of microstructured active materials: Case study of MoSx as a catalyst for the hydrogen evolution reaction, J. Mater. Chem. A, 2021, 9, 22072-22081.</p> <p>C. Iffelsberger and M. Pumera*</p> <p>https://doi.org/10.1039/D1TA05581J</p> <p>Related to the MSCA Project: 888797 LoCatSpot</p>
Correlating NanoSIMS images with ultra-high resolution EM images in Look@NanoSIMS
<p>Supplement of the study by Spataro et al. (2022) describes how to perform correlative image analysis in Look@NanoSIMS. This repository contains files used as an example in that analysis.</p> <p>* Rat7SNR_21.tif = ultra-high resolution EM image (4096 x 3072 pixels)<br> * Spataro-Sept-2018_3.im.zip = zipped raw data produced by NanoSIMS 50L (256 x 256 pixels)<br> * Spataro-Sept-2018_3.zip = zipped folder containing data generated by Look@NanoSIMS</p> <p>To start the analysis, download all files to a folder on your computer (preferably in the same folder) and unzip the file Spataro-Sept-2018_3.zip. The latter step will create a folder Spataro-Sept-2018_3 containing files generated by Look@NanoSIMS when analysing data in Spataro-Sept-2018_3.im.zip and Rat7SNR_21.tif. The files include information about the alignment of individual planes (xyalign.mat), coordinates of the pairs of reference points in the EM and NanoSIMS images (points_10x.mat), regions of interest defined for the resampled (cells_10x.mat) and original NanoSIMS data (cells_1x.mat), and Look@NanoSIMS preferences saved for the resampled (prefs_10x.mat) and original (prefs_1x.mat and prefs.mat) NanoSIMS data. You can use these files to reproduce the analysis described in the Supplement of Spataro et al. (2022).</p> <p>Reference:</p> <p>S. Spataro, B. Maco, S. Escrig, L. Jensen, L. Polerecky, G. Knott, A. Meibom, and B. L. Schneider (2022). Alpha-synuclein-induced changes to neuronal metabolism revealed by stable isotope labeling and ultra-high-resolution imaging.</p>
Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data
<p><strong>Overview:</strong> The Global Inundation Extent from Multi-Satellites (GIEMS; Prigent et al. 2007, Papa et al. 2010) downscaled at 15 arc-second (GIEMS-D15; Fluet-Chouinard et al. 2015) was produced through the downscaling of the GIEMS database (natively at 0.25°). The downscaling procedure predicts the location of surface water cover with an inundation ranking surface generated by bagged decision trees. The decision trees were trained on binary presence/absence of wetland in the GLC2000 global land cover map (Bartholomé & Belward 2005) and used 13 topographic and hydrographic predictors derived from the SRTM-derived HydroSHEDS database (Lehner, Verdin & Jarvis 2008). The downscaling technique to three temporal aggregation of the GIEMS dataset representing three states of land surface inundation extents: mean annual minimum (MA<sub>Min</sub>; total area, 6.5 × 106 km<sup>2</sup>), mean annual maximum (MA<sub>Max</sub>; 12.1 × 106 km<sup>2</sup>), and long-term maximum (LT<sub>Max</sub>; 17.3 × 106 km<sup>2</sup>). The area of MAMin and MAMax from GIEMS were supplemented with the minimum area value from lakes, river and reservoirs from GLWD (Lehner & Döll 2004; classes 1,2,3). LTMax was corrected as the mean area from 3-year rolling maximum from GIEMS and the total wetland area from GLWD (classes 1-12). The accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates. Yet, a comparison against independent regional wetland maps showed adequate agreement over large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than originally offered by GIEMS, allowing for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems.</p> <p><strong>Projection:</strong> WGS84 (EPSG:4326)</p> <p><strong>Geographic extent:</strong></p> <ul> <li>Longitude: -180° to 180°</li> <li>Latitude: -56° to 84°</li> </ul> <p><strong>Spatial resolution: </strong>15 arc-second (500m at equator)</p> <p><strong>Legend</strong> (for discrete pixel values):</p> <ul> <li>0 = Upland</li> <li>1 = Mean Annual Minimum (MA<sub>Min</sub>)</li> <li>2 = Mean Annual Maximum (MA<sub>Max</sub>)</li> <li>3 = Long Term Maximum (LT<sub>Max</sub>)</li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.